People's acceptance and trust in robots are a direct consequence of people's ability to infer and predict the robot's behavior. However, there is no clear consensus on how the legibility of a robot's behavior and explanations should be assessed. In this work, the construct of the Theory of Mind (i.e., the ability to attribute mental states to others) is taken into account and a computerized version of the theory of mind picture sequencing task is presented. Our tool, called the human–robot interaction (HRI) video sequencing task (HRIVST), evaluates the legibility of a robot's behavior toward humans by asking them to order short videos to form a logical sequence of the robot's actions. To validate the proposed metrics, we recruited a sample of 86 healthy subjects. Results showed that the HRIVST has good psychometric properties and is a valuable tool for assessing the legibility of robot behaviors. We also evaluated the effects of symbolic explanations, the presence of a person during the interaction, and the humanoid appearance. Results showed that the interaction condition had no effect on the legibility of the robot's behavior. In contrast, the combination of humanoid robots and explanations seems to result in a better performance of the task.

中文翻译：

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用于机器人行为易读性的人机交互视频排序任务（HRIVST）


人们对机器人的接受和信任是人们推断和预测机器人行为能力的直接结果。然而，对于如何评估机器人行为和解释的易读性，目前还没有明确的共识。在这项工作中，考虑了心理理论的构造（即将心理状态归因于他人的能力），并提出了心理理论图片排序任务的计算机化版本。我们的工具称为人机交互（HRI）视频排序任务（HRIVST），通过要求人类排序短视频以形成机器人动作的逻辑序列来评估机器人对人类行为的易读性。为了验证所提出的指标，我们招募了 86 名健康受试者作为样本。结果表明，HRIVST 具有良好的心理测量特性，是评估机器人行为易读性的有价值的工具。我们还评估了象征性解释、交互过程中人的存在以及人形外观的影响。结果表明，交互条件对机器人行为的清晰度没有影响。相比之下，人形机器人和解释的结合似乎可以更好地完成任务。